Barbara Jane Ericson

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I have been creating free and interactive ebooks for introductory computing courses on the open-source Ruenstone platform and analyzing the clickstream data from those courses to improve the ebooks and instruction. In particular, I am interested in using educational data mining to close the feedback loop and improve the instructional materials. I am also interested in learner sourcing to automatically generate and improve assessments. I have been applying principles from educational psychology such as worked examples plus low cognitive load practice to improve instruction. I have been exploring mixed-up code (Parsons) problems as one type of practice. I created two types of adaptation for Parsons problems: intra-problem and inter-problem. In intra-problem adaptation, if the learner is struggling to solve the current problem it can dynamically be made easier. In inter-problem adaptation the difficulty of the next problem is based on the learner’s performance on the previous problem.

Quan Nguyen

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My research focuses on the application of data science in educational research, so called learning analytics. I have experience analyzing educational data on a large-scale to understand a) how course design influence students’ learning behavior and b) how students form peer networks. My work involves using multiple educational data sources such as log-data in online learning environment, course information, students’ academic records, and location data gathered from campus WiFi networks. I am interested in network analysis, time-series analysis, and machine learning.

Ron Eglash

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Societal control tends to be implemented from the top-down, whether that is a private corporation or a communist state. How can data science empower from the bottom-up? Computational technologies can be designed to replace extractive economies with generative cycles. My research includes AI for the artisanal economy; computational modeling of Indigenous practices; and other means for putting the power of data science in the service of generative justice.

Student moving from her knowledge of braiding algorithms, to her program for braiding patterns, to a mannequin head for installation in adult braider’s shops.

Libby Hemphill

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Dr. Hemphill studies conversations in social media and aims to promote just access to social media spaces and their data. She uses computational approaches to modeling political topics, predicting and addressing toxicity in online discussions, and tracing linguistic adaptations among extremists. She also studies digital data curation and is especially interested in ways to measure and model data reuse so that we can make informed decisions about how to allocate data resources.

Misha Teplitskiy

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My research is at the intersection of Science of Science + Sociology of Organizations + Computational Social Science. I study how social and organizational factors affect scientific discovery. I am especially interested in evaluation practices in science, and whether they promote or stifle innovation. My approach relies primarily on field experiments — interventions in scientific competitions and other settings — and applying computational tools to large-scale observational data.

Current research projects include:
1. Cumulative advantage in science: Do metrics like citation counts and impact factors proxy quality and influence, or help create them?
2. Biases in expert evaluation: Do groups of experts make decisions differently from individuals?
3. Science and the media: What research is picked up by the media, and how is it covered?

Showing how often a paper has been cited causes scientists to perceive it as of lower quality, unless that paper is among the 10% most highly cited.

Kentaro Toyama

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Kentaro Toyama is W. K. Kellogg Professor of Community Information at the University of Michigan School of Information and a fellow of the Dalai Lama Center for Ethics and Transformative Values at MIT. He is the author of “Geek Heresy: Rescuing Social Change from the Cult of Technology.” Toyama conducts interdisciplinary research to understand how the world’s low-income communities interact with digital technology and to invent new ways for technology to support their socio-economic development, including computer simulations of complex systems for policy-making. Previously, Toyama did research in artificial intelligence, computer vision, and human-computer interaction at Microsoft and taught mathematics at Ashesi University in Ghana.

Interacting with children at a Seva Mandir school in Rajasthan, India.

Sarita Schoenebeck

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My work lies at the intersection of human-computer interaction, social computing, social media. I direct the Living Online Lab, where we study and design online technologies that address vital societal issues including justice, equity, harassment, privacy, and dark patterns.

Andrea Thomer

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Andrea Thomer is an assistant professor of information at the University of Michigan School of Information. She conducts research in the areas of data curation, museum informatics, earth science and biodiversity informatics, information organization, and computer supported cooperative work. She is especially interested in how people use and create data and metadata; the impact of information organization on information use; issues of data provenance, reproducibility, and integration; and long-term data curation and infrastructure sustainability. She is studying a number of these issues through the “Migrating Research Data Collections” project – a recently awarded Laura Bush 21st Century Librarianship Early Career Research Grant from the Institute of Museum and Library Services. Dr. Thomer received her doctorate in Library and Information Science from the School of Information Sciences at the University of Illinois at Urbana‐Champaign in 2017.